Abstract

Modeling how concepts are learned from experience is an important
challenge for cognitive science. In cognitive psychology, progressive alignment,
i.e., comparing highly similar examples, has been shown to lead to rapid
learning. In AI, providing very similar negative examples (near-misses) has been
proposed as another way to accelerate learning. This paper describes a model of
concept learning that combines these two ideas, using sketched input to
automatically encode data and reduce tailorability. SAGE, which models
analogical generalization, is used to implement progressive alignment. Near-miss
analysis is modeled by using the Structure Mapping Engine to hypothesize
classification criteria based on differences. This is performed both on labeled
negative examples provided as input, and by using analogical retrieval to find
near-miss examples when positive examples are provided. We use a corpus of
sketches to show that the model can learn concepts based on sketches and that
incorporating near-miss analysis improves learning.